Coronavirus US - CDC All-Cause Death
davegoblue
7/7/2021
Background
This module extends code contained in Coronavirus_Statistics_v004.Rmd to include sourcing of all key functions and parameters. This file includes the latest code for analyzing all-cause death data from CDC Weekly Deaths by Jurisdiction. CDC maintains data on deaths by week, age cohort, and state in the US. Downloaded data are unique by state, epidemiological week, year, age, and type (actual vs. predicted/projected).
These data are known to have a lag between death and reporting, and the CDC back-correct to report deaths at the time the death occurred even if the death is reported in following weeks. This means totals for recent weeks tend to run low (lag), and the CDC run a projection of the expected total number of deaths given the historical lag times. Per other analysts on the internet, there is currently significant supra-lag, with lag times much longer than historical averages causing CDC projected deaths for recent weeks to be low.
The code leverages tidyverse and sourced functions throughout:
# All functions assume that tidyverse and its components are loaded and available
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# If the same function is in both files, use the version from the more specific source
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Excess_Functions_v001.R")Running Code
The main function is readRunCDCAllCause(), which performs multiple tasks:
STEP 0: Optionally, downloads the latest data file from CDC STEP 1: Reads and processes a data file has been downloaded from CDC to local
STEP 2: Extract relevant data from a processed state-level COVID Tracking Project list
STEP 3: Basic plots of the CDC data
STEP 4: Basic excess-deaths analysis
STEP 5: Create cluster-level aggregate plots
STEP 6: Create state-level aggregate plots
STEP 7: Create age-cohort aggregate plots
STEP 8: Returns a list of key data frames, modeling objects, named cluster vectors, etc.
The functions are tested on previously downloaded data:
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20210623.csv"
cdcList_20210703 <- readRunCDCAllCause(loc=cdcLoc,
weekThru=17,
lst=readFromRDS("cdc_daily_210528"),
dlData=FALSE,
stateNoCheck=c("NC"),
pdfCluster=TRUE,
pdfAge=TRUE
)##
## Parameter cvDeathThru has been set as: 2021-05-01
##
##
## *** Data suppression checks ***
## # A tibble: 2 x 6
## noCheck state problem curWeek n deaths
## <lgl> <chr> <lgl> <lgl> <int> <dbl>
## 1 TRUE NC TRUE FALSE 72 NA
## 2 TRUE NC TRUE TRUE 6 NA
## # A tibble: 2 x 3
## noCheck curWeek n
## <lgl> <lgl> <int>
## 1 TRUE FALSE 72
## 2 TRUE TRUE 6
##
##
## Data suppression checks passed
##
##
## *** File has been checked for uniqueness by: state year week age
##
## Rows: 91,537
## Columns: 12
## $ fullState <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala~
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10~
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",~
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,~
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4,~
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years, 75-8~
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015~
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predicted ~
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332, 26,~
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Check Control Levels and Record Counts for Processed Data:
##
##
## Checking variable combination: age
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <fct> <dbl> <dbl> <dbl>
## 1 Under 25 years 10735 0 369164
## 2 25-44 years 13656 0 902390
## 3 45-64 years 16793 0 3549786
## 4 65-74 years 16783 0 3558139
## 5 75-84 years 16790 0 4401133
## 6 85 years and older 16780 0 5681860
##
##
## Checking variable combination: period year Type
## # A tibble: 7 x 6
## period year Type n n_deaths_na deaths
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 14364 0 2691180
## 2 2015-2019 2016 Predicted (weighted) 14445 0 2723236
## 3 2015-2019 2017 Predicted (weighted) 14404 0 2801986
## 4 2015-2019 2018 Predicted (weighted) 14400 0 2830372
## 5 2015-2019 2019 Predicted (weighted) 14415 0 2844025
## 6 2020 2020 Predicted (weighted) 14837 0 3433405
## 7 2021 2021 Predicted (weighted) 4672 0 1138268
##
##
## Checking variable combination: period Suppress
## # A tibble: 3 x 5
## period Suppress n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 <NA> 72028 0 13890799
## 2 2020 <NA> 14837 0 3433405
## 3 2021 <NA> 4672 0 1138268
##
##
## Checking variable combination: period Note
## # A tibble: 9 x 5
## period Note n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-20~ <NA> 72028 0 1.39e7
## 2 2020 Data in recent weeks are incomplete. Only ~ 13194 0 2.96e6
## 3 2020 Data in recent weeks are incomplete. Only ~ 531 0 2.31e5
## 4 2020 Weighted numbers of deaths are 20% or more~ 280 0 6.00e4
## 5 2020 Weights may be too low to account for unde~ 18 0 9.85e3
## 6 2020 <NA> 814 0 1.69e5
## 7 2021 Data in recent weeks are incomplete. Only ~ 4469 0 1.10e6
## 8 2021 Data in recent weeks are incomplete. Only ~ 14 0 9.65e2
## 9 2021 Data in recent weeks are incomplete. Only ~ 189 0 3.58e4
##
## *** File has been checked for uniqueness by: cluster year week
##
## Plots will be run after excluding stateNoCheck states
##
## Detailed cluster summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_cluster_2021w17.pdf
##
## Returning plot outputs to the main log file
## Joining, by = "state"
##
## Detailed age summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_age_2021w17.pdf
##
## Returning plot outputs to the main log file
The latest data are downloaded and processed:
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20210708.csv"
cdcList_20210708 <- readRunCDCAllCause(loc=cdcLoc,
weekThru=22,
lst=readFromRDS("cdc_daily_210708"),
stateNoCheck=c("NC", "AK", "WV"),
pdfCluster=TRUE,
pdfAge=TRUE
)##
## Parameter cvDeathThru has been set as: 2021-06-05
##
##
## *** Data suppression checks ***
## # A tibble: 4 x 6
## noCheck state problem curWeek n deaths
## <lgl> <chr> <lgl> <lgl> <int> <dbl>
## 1 TRUE AK TRUE FALSE 2 NA
## 2 TRUE NC TRUE FALSE 102 NA
## 3 TRUE NC TRUE TRUE 6 NA
## 4 TRUE WV TRUE TRUE 2 NA
## # A tibble: 2 x 3
## noCheck curWeek n
## <lgl> <lgl> <int>
## 1 TRUE FALSE 104
## 2 TRUE TRUE 8
##
##
## Data suppression checks passed
##
##
## *** File has been checked for uniqueness by: state year week age
##
## Rows: 92,880
## Columns: 12
## $ fullState <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala~
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10~
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",~
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,~
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4,~
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years, 75-8~
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015~
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predicted ~
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332, 26,~
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Check Control Levels and Record Counts for Processed Data:
##
##
## Checking variable combination: age
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <fct> <dbl> <dbl> <dbl>
## 1 Under 25 years 10890 0 374959
## 2 25-44 years 13868 0 919211
## 3 45-64 years 17038 0 3605423
## 4 65-74 years 17027 0 3615820
## 5 75-84 years 17033 0 4467166
## 6 85 years and older 17024 0 5757892
##
##
## Checking variable combination: period year Type
## # A tibble: 7 x 6
## period year Type n n_deaths_na deaths
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 14364 0 2691176
## 2 2015-2019 2016 Predicted (weighted) 14443 0 2723213
## 3 2015-2019 2017 Predicted (weighted) 14408 0 2802027
## 4 2015-2019 2018 Predicted (weighted) 14400 0 2830376
## 5 2015-2019 2019 Predicted (weighted) 14414 0 2844003
## 6 2020 2020 Predicted (weighted) 14838 0 3432903
## 7 2021 2021 Predicted (weighted) 6013 0 1416773
##
##
## Checking variable combination: period Suppress
## # A tibble: 3 x 5
## period Suppress n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 <NA> 72029 0 13890795
## 2 2020 <NA> 14838 0 3432903
## 3 2021 <NA> 6013 0 1416773
##
##
## Checking variable combination: period Note
## # A tibble: 10 x 5
## period Note n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-20~ <NA> 72029 0 1.39e7
## 2 2020 Data in recent weeks are incomplete. Only~ 13459 0 3.04e6
## 3 2020 Data in recent weeks are incomplete. Only~ 5 0 1.24e2
## 4 2020 Data in recent weeks are incomplete. Only~ 262 0 1.57e5
## 5 2020 Weighted numbers of deaths are 20% or mor~ 280 0 6.00e4
## 6 2020 Weights may be too low to account for und~ 10 0 5.95e3
## 7 2020 <NA> 822 0 1.73e5
## 8 2021 Data in recent weeks are incomplete. Only~ 5631 0 1.34e6
## 9 2021 Data in recent weeks are incomplete. Only~ 24 0 2.00e3
## 10 2021 Data in recent weeks are incomplete. Only~ 358 0 7.15e4
##
## *** File has been checked for uniqueness by: cluster year week
##
## Plots will be run after excluding stateNoCheck states
##
## Detailed cluster summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_cluster_2021w22.pdf
##
## Returning plot outputs to the main log file
## Joining, by = "state"
##
## Detailed age summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_age_2021w22.pdf
##
## Returning plot outputs to the main log file
saveToRDS(cdcList_20210708)The function readProcessCDC() is updated to allow for more control in zeroing out (rather than erroring) where there is a small number of data suppression:
# Function to check for CDC excess suppression
checkCDCSuppression <- function(df, stateNoCheck, errTotAllowed=20, errMaxAllowed=round(errTotAllowed/2)) {
# Categorize the potential issues in the file (note to suppress or NA deaths)
checkProblems <- df %>%
mutate(problem=(!is.na(Suppress) | is.na(deaths)),
noCheck=state %in% all_of(stateNoCheck)
)
# Print a list of the problems, excluding those in stateNoCheck
cat("\nRows in states to be checked that have NA deaths or a note for suppression:\n")
checkProblems %>%
filter(problem, !noCheck) %>%
arrange(desc(year), desc(week)) %>%
select(state, weekEnding, year, week, age, Suppress, deaths) %>%
as.data.frame() %>%
print()
# Summarize the problems
cat("\n\nProblems by state:\n")
checkProblems %>%
group_by(noCheck, state, problem) %>%
summarize(n=n(), deaths=specNA(sum)(deaths), .groups="drop") %>%
filter(problem) %>%
print()
# Assess the amount of error
errorState <- checkProblems %>%
filter(problem, !noCheck) %>%
count(state)
# Error out if threshold for error by state OR total errors exceeded
errMax <- errorState %>% pull(n) %>% max()
errTot <- errorState %>% pull(n) %>% sum()
cat("\n\nThere are", errTot, "rows with errors; maximum for any given state is", errMax, "errors\n")
if ((errTot > errTotAllowed) | (errMax > errMaxAllowed)) {
stop("\nToo many errors; thresholds are ", errTotAllowed, " total and ", errMaxAllowed, " maximum\n")
}
}
plotQCReadProcessCDC <- function(df,
ckCombos=list(c("age"), c("period", "year", "Type"),
c("period", "Suppress"), c("period", "Note")
)
) {
# Create dataset for analysis
df <- df %>%
mutate(n=1, n_deaths_na=ifelse(is.na(deaths), 1, 0))
# Check control totals by specified combinaions
purrr::walk(ckCombos, .f=function(x) {
cat("\n\nChecking variable combination:", x, "\n")
checkControl(df, groupBy=x, useVars=c("n", "n_deaths_na", "deaths"), fn=specNA(sum))
}
)
# Plot deaths by state
p1 <- checkControl(df,
groupBy=c("state"),
useVars=c("deaths"),
fn=specNA(sum),
printControls=FALSE,
pivotData=FALSE
) %>%
ggplot(aes(x=fct_reorder(state, deaths), y=deaths)) +
geom_col(fill="lightblue") +
geom_text(aes(y=deaths, label=paste0(round(deaths/1000), "k")), hjust=0, size=3) +
coord_flip() +
labs(y="Total deaths", x=NULL, title="Total deaths by state in all years in processed file")
print(p1)
# Plot deaths by week/year
p2 <- checkControl(df,
groupBy=c("year", "week"),
useVars=c("deaths"),
fn=specNA(sum),
printControls=FALSE,
pivotData=FALSE
) %>%
ggplot(aes(x=week, y=deaths)) +
geom_line(aes(group=year, color=year)) +
labs(title="Deaths by year and epidemiological week", x="Epi week", y="US deaths") +
scale_color_discrete("Year") +
lims(y=c(0, NA))
print(p2)
}
# Function to read and process raw CDC all-cause deaths data
readProcessCDC <- function(fName,
weekThru,
periodKeep=cdcExcessParams$periodKeep,
fDir="./RInputFiles/Coronavirus/",
col_types=cdcExcessParams$colTypes,
renameVars=cdcExcessParams$remapVars,
maxSuppressAllowed=20,
stateNoCheck=c()
) {
# FUNCTION ARGUMENTS:
# fName: name of the downloaded CDC data file
# weekThru: any record where week is less than or equal to weekThru will be kept
# periodKeep: any record where period is in periodKeep will be kept
# fDir: directory name for the downloaded CDC data file
# col_types: variable type by column in the CDC data (passed to readr::read_csv())
# renameVars: named vector for variable renaming of type c("Existing Name"="New Name")
# maxSuppressAllowed: maximum number of data suppressions (must be in current week/year) to avoid error
# stateNoCheck: vector of states that do NOT have suppression errors thrown
# STEP 1: Read the CSV data
cdcRaw <- fileRead(paste0(fDir, fName), col_types=col_types)
# glimpse(cdcRaw)
# STEP 2: Rename the variables for easier interpretation
cdcRenamed <- cdcRaw %>%
colRenamer(vecRename=renameVars) %>%
colMutater(selfList=list("weekEnding"=lubridate::mdy))
# glimpse(cdcRenamed)
# STEP 3: Convert to factored data
cdcFactored <- cdcRenamed %>%
colMutater(selfList=list("age"=factor), levels=cdcExcessParams$ageLevels) %>%
colMutater(selfList=list("period"=factor), levels=cdcExcessParams$periodLevels) %>%
colMutater(selfList=list("year"=factor), levels=cdcExcessParams$yearLevels)
# glimpse(cdcFactored)
# STEP 4: Filter the data to include only weighted deaths and only through the desired time period
cdcFiltered <- cdcFactored %>%
rowFilter(lstFilter=list("Type"="Predicted (weighted)")) %>%
filter(period %in% all_of(periodKeep) | week <= weekThru)
# glimpse(cdcFiltered)
# STEP 4a: Check that all suppressed data and NA deaths have been eliminated
cat("\n\n *** Data suppression checks *** \n")
checkCDCSuppression(cdcFiltered, stateNoCheck=stateNoCheck, errTotAllowed=maxSuppressAllowed)
cat("\n\nData suppression checks passed\n\n")
# STEP 5: Remove any NA death fields, delete the US record, convert YC to be part of NY
cdcProcessed <- cdcFiltered %>%
rowFilter(lstExclude=list("state"=c("US", "PR"), "deaths"=c(NA))) %>%
mutate(state=ifelse(state=="YC", "NY", state),
fullState=ifelse(state %in% c("NY", "YC"), "New York State (NY plus YC)", fullState)
) %>%
group_by(fullState, weekEnding, state, year, week, age, period, Type, Suppress) %>%
arrange(!is.na(Note)) %>%
summarize(n=n(), deaths=sum(deaths), Note=first(Note), .groups="drop") %>%
ungroup() %>%
checkUniqueRows(uniqueBy=c("state", "year", "week", "age"))
glimpse(cdcProcessed)
# STEP 5a: Check control levels for key variables in processed file
cat("\nCheck Control Levels and Record Counts for Processed Data:\n")
plotQCReadProcessCDC(cdcProcessed)
# STEP 6: Return the processed data file
cdcProcessed
}The data are processed using the updated function:
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20210708.csv"
cdcList_20210708_v2 <- readRunCDCAllCause(loc=cdcLoc,
weekThru=23,
lst=readFromRDS("cdc_daily_210708"),
stateNoCheck=c("NC"),
pdfCluster=TRUE,
pdfAge=TRUE
)##
## Parameter cvDeathThru has been set as: 2021-06-12
##
##
## *** Data suppression checks ***
##
## Rows in states to be checked that have NA deaths or a note for suppression:
## state weekEnding year week age
## 1 CT 2021-06-12 2021 23 45-64 years
## 2 CT 2021-06-12 2021 23 65-74 years
## 3 CT 2021-06-12 2021 23 75-84 years
## 4 CT 2021-06-12 2021 23 85 years and older
## 5 DE 2021-06-12 2021 23 65-74 years
## 6 DE 2021-06-12 2021 23 75-84 years
## 7 DE 2021-06-12 2021 23 85 years and older
## 8 WV 2021-06-05 2021 22 45-64 years
## 9 WV 2021-06-05 2021 22 65-74 years
## 10 AK 2021-05-08 2021 18 45-64 years
## 11 AK 2021-05-08 2021 18 65-74 years
## Suppress deaths
## 1 Suppressed (counts highly incomplete, <50% of expected) NA
## 2 Suppressed (counts highly incomplete, <50% of expected) NA
## 3 Suppressed (counts highly incomplete, <50% of expected) NA
## 4 Suppressed (counts highly incomplete, <50% of expected) NA
## 5 Suppressed (counts highly incomplete, <50% of expected) NA
## 6 Suppressed (counts highly incomplete, <50% of expected) NA
## 7 Suppressed (counts highly incomplete, <50% of expected) NA
## 8 Suppressed (counts highly incomplete, <50% of expected) NA
## 9 Suppressed (counts highly incomplete, <50% of expected) NA
## 10 Suppressed (counts highly incomplete, <50% of expected) NA
## 11 Suppressed (counts highly incomplete, <50% of expected) NA
##
##
## Problems by state:
## # A tibble: 5 x 5
## noCheck state problem n deaths
## <lgl> <chr> <lgl> <int> <dbl>
## 1 FALSE AK TRUE 2 NA
## 2 FALSE CT TRUE 4 NA
## 3 FALSE DE TRUE 3 NA
## 4 FALSE WV TRUE 2 NA
## 5 TRUE NC TRUE 114 NA
##
##
## There are 11 rows with errors; maximum for any given state is 4 errors
##
##
## Data suppression checks passed
##
##
## *** File has been checked for uniqueness by: state year week age
##
## Rows: 93,132
## Columns: 12
## $ fullState <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala~
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10~
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",~
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,~
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4,~
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years, 75-8~
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015~
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predicted ~
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332, 26,~
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Check Control Levels and Record Counts for Processed Data:
##
##
## Checking variable combination: age
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <fct> <dbl> <dbl> <dbl>
## 1 Under 25 years 10919 0 375951
## 2 25-44 years 13908 0 922283
## 3 45-64 years 17084 0 3615594
## 4 65-74 years 17072 0 3626546
## 5 75-84 years 17079 0 4479686
## 6 85 years and older 17070 0 5772387
##
##
## Checking variable combination: period year Type
## # A tibble: 7 x 6
## period year Type n n_deaths_na deaths
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 14364 0 2691176
## 2 2015-2019 2016 Predicted (weighted) 14443 0 2723213
## 3 2015-2019 2017 Predicted (weighted) 14408 0 2802027
## 4 2015-2019 2018 Predicted (weighted) 14400 0 2830376
## 5 2015-2019 2019 Predicted (weighted) 14414 0 2844003
## 6 2020 2020 Predicted (weighted) 14838 0 3432903
## 7 2021 2021 Predicted (weighted) 6265 0 1468749
##
##
## Checking variable combination: period Suppress
## # A tibble: 3 x 5
## period Suppress n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-2019 <NA> 72029 0 13890795
## 2 2020 <NA> 14838 0 3432903
## 3 2021 <NA> 6265 0 1468749
##
##
## Checking variable combination: period Note
## # A tibble: 10 x 5
## period Note n n_deaths_na deaths
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 2015-20~ <NA> 72029 0 1.39e7
## 2 2020 Data in recent weeks are incomplete. Only~ 13459 0 3.04e6
## 3 2020 Data in recent weeks are incomplete. Only~ 5 0 1.24e2
## 4 2020 Data in recent weeks are incomplete. Only~ 262 0 1.57e5
## 5 2020 Weighted numbers of deaths are 20% or mor~ 280 0 6.00e4
## 6 2020 Weights may be too low to account for und~ 10 0 5.95e3
## 7 2020 <NA> 822 0 1.73e5
## 8 2021 Data in recent weeks are incomplete. Only~ 5822 0 1.38e6
## 9 2021 Data in recent weeks are incomplete. Only~ 34 0 3.23e3
## 10 2021 Data in recent weeks are incomplete. Only~ 409 0 8.16e4
##
## *** File has been checked for uniqueness by: cluster year week
##
## Plots will be run after excluding stateNoCheck states
##
## Detailed cluster summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_cluster_2021w23.pdf
##
## Returning plot outputs to the main log file
## Joining, by = "state"
##
## Detailed age summary PDF file is available at: ./RInputFiles/Coronavirus/Plots/CDC_age_2021w23.pdf
##
## Returning plot outputs to the main log file
